基于DMSP-OLS数据和可持续生计的中国农村多维贫困空间识别

贫困是人类在21世纪发展所要长期面对的困境,消除贫困是中国全面建成小康社会的最大挑战。单纯依据经济、社会统计数据对贫困状况的测度缺乏空间视角,无法直观地分析贫困状况的空间差异、地域特征及致贫原因的生态地理背景分异机制。引入脆弱性-可持续生计框架模型,构建了可持续生计的测度指标体系,以宁夏回族自治区所辖县区为样本,借助遥感和GIS空间分析技术,建立了2002年和2013年夜间灯光指数和可持续生计指数(SLI)间的回归模型,并以青海省所有县区的数据对模型进行了验证。利用检验后的模型将持续生计指数空间化;采用空间自相关分析了县域持续生计指数的空间集聚状况。结果表明:2002年和2013年估算的SLI平均相对误差为10.84%和12.19%,精度较高。中国所有县区SLI的全局Moran's I值分别为0.636和0.579,具有较高的空间依赖性,"低-低"型贫困区域即空间贫困陷阱区集中在扎兰屯-百色一线以西。两时点上分别识别出642个、612个多维贫困县。2013年,多维贫困县区在空间上表现为东、中、西部岛状、块状、连片状3种地域类型。研究发现夜间灯光数据是一种有效的空间贫困测度数据源,可实现数据缺乏地区大尺度上的多维贫困动态监测。

Spatial identification of multi-dimensional poverty in rural China by using nighttime light and sustainable livelihoods

Poverty has become one of the long-term predicaments affecting the development of human society during the 21^st century. Eliminating poverty in rural areas in 2020 under the current standards and addressing the regional overall poverty are the most difficult challenges to build a sustainable society across China. Since traditional statistical data on the socio-economic conditions have been limited by the lack of information regarding the area being studied and the time-consuming nature of data collection, and because objectivity has been difficult to be guaranteed, this method cannot meet the demand for large-scale, long-term, and dynamic research for addressing the problem of poverty. Developing methods for measuring multi-dimensional poverty and improving the accuracy of poverty identification are the key issues for improving the quality and effectiveness of rural poverty reduction programs in China. In light of the academic thoughts of the vulnerability and sustainability of livelihood analysis framework, this study established an index system and combined 30 variables into a sustainable livelihoods index (SLI), including 22 counties of Ningxia Hui Autonomous Region as a sample, to reflect the multiple factors that affect the livelihood of farmers. Regression models have been used to verify the correlation between nighttime light index and SLI. In 2002 and 2013, the model was tested in 43 counties of Qinghai Province, and the estimates had an average relative error of only 10.84% and 12.19%, respectively. This efficient method has good practical applicability and relatively high measurement precision for multi-dimensional spatial poverty identification. Spatial clustering effect of ecological poverty was analyzed using the explore spatial data analysis (ESDA) method. A significant spatial auto-correlation was noted for sustainable livelihood, since Moran''s I index in 2002 and 2013 was 0.636 and 0.579, respectively, which indicates that poverty of neighboring counties has a positive effect on the poverty of a specific county. Both High-High and Low-Low areas are distributed intensively, whereas both High-Low and Low-High areas are distributed discretely. Some counties with a Low-Low SLI pattern fall into the spatial trap of poverty based on the results of Local Moran''s I index. These counties are located in the western parts of the Zaerdong-Bose Line. Geographical identification of multi-dimensional spatial poverty in rural China was performed at the grid and county levels. In China, 642 and 612 multi-dimensional poverty counties were recorded in 2002 and 2013, respectively. During 2002-2013, the contiguous and concentrated distribution of poverty-stricken areas has not changed significantly. In 2013, the multi-dimensional poverty counties showed 3 spatial areal patterns:island distribution in eastern China, massive distribution in central China, and contiguous distribution in western China. The multi-dimensional poverty counties need to develop different policies to overcome poverty based on regional sustainable livelihood capacity and development potential. This method can improve the accuracy of targeting and identifying multi-dimensional poverty areas and could surpass the current Poverty-Targeting-Alleviation (jing zhun fu pin) initiatives dominating the poverty-reduction policy of China''s government. To reduce multi-dimensional poverty, China needs to have a clear regional development strategy that favors disadvantaged areas. The core implication is to combine region-based strategy and people-based policy.